Hi! I'm Johnny Hu
M.S. Electrical Engineering (Management) at King’s College London, with a biomedical engineering and computer science background. I build LLM/RAG systems, cloud-native ML pipelines, and diagnostic tools that turn clinical data into real-time decisions.
200+
Hours Audio Processed
10k+
Transcript Segments Indexed
92%+
Model Validation Accuracy
Johnny Hu
About Me
I’m an engineer with a background in Computer Science and Biomedical Engineering, currently pursuing an M.S. in Electrical Engineering with Management at King’s College London. I specialize in AI/ML, LLM/RAG systems, and cloud-native healthcare platforms. My goal is to translate clinical data into reliable, real-time diagnostic tools.
karta1337366@gmail.com
Phone
+44 07722177083
Languages
English (Professional), Mandarin (Native)
Services
From code to care, from biochips to browsers, I craft intelligent healthcare systems — where software meets science, and precision meets purpose.
Design and deploy ML models for diagnostics, forecasting, and clinical decision support.
Build retrieval-augmented generation and multi-agent workflows for domain-specific knowledge tasks.
Develop web apps and APIs for data entry, visualization, and model delivery (Flask, React).
Deploy scalable pipelines with AWS/Azure, Docker, and Kubernetes for production ML.
Clean, structure, and analyze complex datasets for reliable training and evaluation.
Bridge biosensing, medical devices, and software for real-time monitoring workflows.
Life Time
Building intelligent healthcare systems across AI/ML, cloud, and biomedical engineering.
Relevant coursework: Digital IC Design, Semiconductor Fabrication, DSP, Sensors/Actuators, Renewable Power Systems, Project & Technology Management.
King’s College London, London, UK
Built real-time transcription pipelines with Node.js and FastAPI, indexing 200+ hours of medical conference audio into 10k+ transcript segments for LLM fine-tuning and analytics.
Anivance AI, Hsinchu, Taiwan
Thesis focused on a web-based prosthesis joint infection diagnostic system combining interpretable ML and cloud-native deployment.
National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Built Python data pipelines and automated labeling with AWS SageMaker Ground Truth, improving throughput and labeling accuracy for irregular images.
Flow, Inc., Taipei, Taiwan
Interdisciplinary training in software engineering, AI, and smart healthcare systems. Also completed a B.S. in Healthcare Administration and Medical Information at Kaohsiung Medical University.
National Sun Yat-sen University, Kaohsiung, Taiwan
Built an Android inspection app integrated with ASP.NET Web APIs to digitize safety workflows, reduce inspection time, and enable real-time machine data visualization.
AU Optronics Corporation, Taichung, Taiwan
Skills & Awards
AI/ML systems, full-stack engineering, and cloud-native deployment across healthcare and manufacturing.
Python / Data
Advanced
PyTorch / TensorFlow
Advanced
AWS / Azure
Proficient
Docker / K8s
Proficient
React / Flask
Proficient
Selected work
End-to-end systems combining ML, data engineering, and full-stack delivery.
Master’s Thesis
Web-based PJI Diagnostic SystemFull-stack platform with interpretable ML (XGBoost/RF/SVM), Dockerized deployment, and real-time diagnostic visualization.
Competition
National Intelligent Manufacturing Contest (Silver Medal)Time-series analysis and image classification on 10k+ samples; ensemble CNNs achieved 92% validation accuracy.
Research Project
EchoGuard — Voice-Activated Emergency ReportingAndroid prototype with voice-to-action workflow and tamper-evident reporting via IPFS.
Leadership
"Taught AI in medical applications to 100+ students per semester, covering SVM, CNN, and reinforcement learning with hands-on labs."
Teaching Assistant
Kaohsiung Medical University (Feb. 2020 – Jan. 2021)
"Built embedded sensing and ML models for pressure ulcer risk monitoring, improving prediction accuracy under sparse clinical data."
Research Assistant
Prof. Wen-Hsien He’s Lab (Sep. 2018 – Jul. 2019)
